Overview

Dataset statistics

Number of variables9
Number of observations2178088
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory116.3 MiB
Average record size in memory56.0 B

Variable types

NUM8
CAT1

Warnings

Valeur fonciere is highly skewed (γ1 = 102.6400225) Skewed
Surface reelle bati is highly skewed (γ1 = 209.2974261) Skewed
Surface terrain is highly skewed (γ1 = 35.74357122) Skewed
df_index has unique values Unique
Surface reelle bati has 1271835 (58.4%) zeros Zeros
Nombre pieces principales has 1359371 (62.4%) zeros Zeros
Surface terrain has 589383 (27.1%) zeros Zeros

Reproduction

Analysis started2020-10-21 23:54:59.974129
Analysis finished2020-10-22 00:01:19.604730
Duration6 minutes and 19.63 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2178088
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1192718.357
Minimum0
Maximum2407040
Zeros1
Zeros (%)< 0.1%
Memory size16.6 MiB
2020-10-22T02:01:22.969118image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile120277.35
Q1599915.75
median1200638.5
Q31780231.25
95-th percentile2268533.65
Maximum2407040
Range2407040
Interquartile range (IQR)1180315.5

Descriptive statistics

Standard deviation688239.2687
Coefficient of variation (CV)0.5770341879
Kurtosis-1.179442915
Mean1192718.357
Median Absolute Deviation (MAD)587222.5
Skewness-0.009760212173
Sum2.597845541e+12
Variance4.73673291e+11
MonotocityStrictly increasing
2020-10-22T02:01:23.440847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
21500671< 0.1%
 
21295731< 0.1%
 
21234301< 0.1%
 
21254791< 0.1%
 
21029521< 0.1%
 
21050011< 0.1%
 
20988581< 0.1%
 
21009071< 0.1%
 
21111481< 0.1%
 
Other values (2178078)2178078> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
24070401< 0.1%
 
24070391< 0.1%
 
24070381< 0.1%
 
24070371< 0.1%
 
24070361< 0.1%
 

Nature mutation
Categorical

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.6 MiB
Vente
2115098 
Vente en l'état futur d'achèvement
 
26753
Echange
 
23993
Vente terrain à bâtir
 
7493
Adjudication
 
4315
ValueCountFrequency (%) 
Vente211509897.1%
 
Vente en l'état futur d'achèvement267531.2%
 
Echange239931.1%
 
Vente terrain à bâtir74930.3%
 
Adjudication43150.2%
 
Expropriation436< 0.1%
 
2020-10-22T02:01:24.027512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-10-22T02:01:24.595186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:01:25.505664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length34
Median length5
Mean length5.448744036
Min length5

Code type local
Real number (ℝ≥0)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.274960424
Minimum1
Maximum5
Zeros0
Zeros (%)0.0%
Memory size8.3 MiB
2020-10-22T02:01:26.186275image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile5
Maximum5
Range4
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.655801634
Coefficient of variation (CV)0.505594395
Kurtosis-1.61044209
Mean3.274960424
Median Absolute Deviation (MAD)2
Skewness-0.1947858314
Sum7133152
Variance2.741679052
MonotocityNot monotonic
2020-10-22T02:01:26.515084image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=5)
ValueCountFrequency (%) 
592122642.3%
 
151351623.6%
 
334602815.9%
 
230692514.1%
 
4903934.2%
 
ValueCountFrequency (%) 
151351623.6%
 
230692514.1%
 
334602815.9%
 
4903934.2%
 
592122642.3%
 
ValueCountFrequency (%) 
592122642.3%
 
4903934.2%
 
334602815.9%
 
230692514.1%
 
151351623.6%
 

Valeur fonciere
Real number (ℝ≥0)

SKEWED

Distinct89486
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean569607.0484
Minimum0.01
Maximum2086000000
Zeros0
Zeros (%)0.0%
Memory size16.6 MiB
2020-10-22T02:01:26.989837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2500
Q155000
median139766
Q3250500
95-th percentile850000
Maximum2086000000
Range2086000000
Interquartile range (IQR)195500

Descriptive statistics

Standard deviation6047919.211
Coefficient of variation (CV)10.61770431
Kurtosis25137.02172
Mean569607.0484
Median Absolute Deviation (MAD)94384
Skewness102.6400225
Sum1.240654277e+12
Variance3.657732678e+13
MonotocityNot monotonic
2020-10-22T02:01:27.485530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
150000195490.9%
 
100000195190.9%
 
120000183050.8%
 
80000163830.8%
 
130000162690.7%
 
110000160850.7%
 
1159120.7%
 
140000158630.7%
 
200000154290.7%
 
50000150430.7%
 
Other values (89476)200973192.3%
 
ValueCountFrequency (%) 
0.012< 0.1%
 
0.15117< 0.1%
 
0.166< 0.1%
 
0.182< 0.1%
 
0.192< 0.1%
 
ValueCountFrequency (%) 
20860000002< 0.1%
 
17500000003< 0.1%
 
69018675024< 0.1%
 
6129904606< 0.1%
 
4000000001< 0.1%
 

Latitude
Real number (ℝ≥0)

Distinct27878
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.9070795
Minimum41.43549874
Maximum51.07304369
Zeros0
Zeros (%)0.0%
Memory size16.6 MiB
2020-10-22T02:01:28.007232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum41.43549874
5-th percentile43.40267912
Q145.00720159
median47.26511299
Q348.70588802
95-th percentile50.37764601
Maximum51.07304369
Range9.637544944
Interquartile range (IQR)3.698686423

Descriptive statistics

Standard deviation2.159481633
Coefficient of variation (CV)0.04603743519
Kurtosis-1.050324024
Mean46.9070795
Median Absolute Deviation (MAD)1.618849978
Skewness-0.1970060188
Sum102167747
Variance4.663360921
MonotocityNot monotonic
2020-10-22T02:01:28.501947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
43.59638143654963.0%
 
44.85724454476902.2%
 
47.23163568396361.8%
 
50.63171832386951.8%
 
49.4984525142980.7%
 
43.84493839125780.6%
 
45.43012355125400.6%
 
47.39863823118440.5%
 
45.85425496110520.5%
 
47.98852567100300.5%
 
Other values (27868)191422987.9%
 
ValueCountFrequency (%) 
41.4354987457< 0.1%
 
41.5046258320< 0.1%
 
41.5163043838< 0.1%
 
41.524931946< 0.1%
 
41.5559603734< 0.1%
 
ValueCountFrequency (%) 
51.07304369130< 0.1%
 
51.063772099< 0.1%
 
51.0486014558< 0.1%
 
51.0479753648< 0.1%
 
51.0307229152650.2%
 

Longitude
Real number (ℝ)

Distinct27878
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.605260636
Minimum-5.085345335
Maximum9.510244977
Zeros0
Zeros (%)0.0%
Memory size16.6 MiB
2020-10-22T02:01:28.963682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-5.085345335
5-th percentile-2.173420587
Q1-0.4310759606
median1.754946717
Q33.100935838
95-th percentile5.932396342
Maximum9.510244977
Range14.59559031
Interquartile range (IQR)3.532011799

Descriptive statistics

Standard deviation2.461188802
Coefficient of variation (CV)1.533201991
Kurtosis-0.4921641444
Mean1.605260636
Median Absolute Deviation (MAD)1.733703777
Skewness0.03690341499
Sum3496398.928
Variance6.05745032
MonotocityNot monotonic
2020-10-22T02:01:29.526359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.431672934654963.0%
 
-0.5736967812476902.2%
 
-1.548310086396361.8%
 
3.047832723386951.8%
 
0.1401537192142980.7%
 
4.34806797125780.6%
 
4.379139971125400.6%
 
0.6965263764118440.5%
 
1.248757902110520.5%
 
0.2000304935100300.5%
 
Other values (27868)191422987.9%
 
ValueCountFrequency (%) 
-5.08534533549< 0.1%
 
-4.764636374111< 0.1%
 
-4.76234204197< 0.1%
 
-4.7535248390< 0.1%
 
-4.73355077369< 0.1%
 
ValueCountFrequency (%) 
9.5102449778< 0.1%
 
9.4732767931< 0.1%
 
9.45690314411< 0.1%
 
9.4422495821< 0.1%
 
9.4273793332< 0.1%
 

Surface reelle bati
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct3679
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.82587389
Minimum0
Maximum240000
Zeros1271835
Zeros (%)58.4%
Memory size8.3 MiB
2020-10-22T02:01:29.975103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q367
95-th percentile144
Maximum240000
Range240000
Interquartile range (IQR)67

Descriptive statistics

Standard deviation619.9422564
Coefficient of variation (CV)12.44217528
Kurtosis62231.97131
Mean49.82587389
Median Absolute Deviation (MAD)0
Skewness209.2974261
Sum108525138
Variance384328.4013
MonotocityNot monotonic
2020-10-22T02:01:30.404857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0127183558.4%
 
80181220.8%
 
90168830.8%
 
60159720.7%
 
70151930.7%
 
100147160.7%
 
50128470.6%
 
40117820.5%
 
65111380.5%
 
120109260.5%
 
Other values (3669)77867435.8%
 
ValueCountFrequency (%) 
0127183558.4%
 
1307< 0.1%
 
2289< 0.1%
 
3214< 0.1%
 
4170< 0.1%
 
ValueCountFrequency (%) 
2400002< 0.1%
 
2150002< 0.1%
 
2121202< 0.1%
 
1528566< 0.1%
 
1425361< 0.1%
 

Nombre pieces principales
Real number (ℝ≥0)

ZEROS

Distinct40
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.336657197
Minimum0
Maximum67
Zeros1359371
Zeros (%)62.4%
Memory size8.3 MiB
2020-10-22T02:01:30.795177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile5
Maximum67
Range67
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.00033039
Coefficient of variation (CV)1.496517129
Kurtosis3.001539706
Mean1.336657197
Median Absolute Deviation (MAD)0
Skewness1.398016025
Sum2911357
Variance4.001321671
MonotocityNot monotonic
2020-10-22T02:01:31.177935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
0135937162.4%
 
41964239.0%
 
31790608.2%
 
21375426.3%
 
51298086.0%
 
1895884.1%
 
6534832.5%
 
7200030.9%
 
875550.3%
 
928000.1%
 
Other values (30)24550.1%
 
ValueCountFrequency (%) 
0135937162.4%
 
1895884.1%
 
21375426.3%
 
31790608.2%
 
41964239.0%
 
ValueCountFrequency (%) 
671< 0.1%
 
561< 0.1%
 
541< 0.1%
 
532< 0.1%
 
501< 0.1%
 

Surface terrain
Real number (ℝ≥0)

SKEWED
ZEROS

Distinct39745
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1965.308289
Minimum0
Maximum1662560
Zeros589383
Zeros (%)27.1%
Memory size8.3 MiB
2020-10-22T02:01:31.675650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median292
Q31000
95-th percentile8510
Maximum1662560
Range1662560
Interquartile range (IQR)1000

Descriptive statistics

Standard deviation8947.693457
Coefficient of variation (CV)4.552819275
Kurtosis3473.886753
Mean1965.308289
Median Absolute Deviation (MAD)292
Skewness35.74357122
Sum-14352896
Variance80061218.21
MonotocityNot monotonic
2020-10-22T02:01:32.188381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
058938327.1%
 
500317341.5%
 
1000142210.7%
 
60046080.2%
 
80045360.2%
 
1343490.2%
 
1243430.2%
 
40039440.2%
 
20038160.2%
 
10037250.2%
 
Other values (39735)151342969.5%
 
ValueCountFrequency (%) 
058938327.1%
 
135610.2%
 
228710.1%
 
324990.1%
 
426520.1%
 
ValueCountFrequency (%) 
16625601< 0.1%
 
14203881< 0.1%
 
14115244< 0.1%
 
12502231< 0.1%
 
11877671< 0.1%
 

Interactions

2020-10-22T01:58:03.962527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:06.617540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:09.722761image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:12.550140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:15.066701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:17.362420image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:19.716069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:22.064762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:24.680260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:27.457668image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:29.945014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:32.445581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:34.990123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:37.630613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:40.556934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:43.581686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:46.964753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:49.426340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:52.180765image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:54.711312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:58:57.951092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:00.754764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:03.504265image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:06.510091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:09.488099image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:12.133586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:14.998945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:17.546770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:20.139283image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:22.799762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:25.981943image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:28.775342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:31.255921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:33.670031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:36.138357image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:38.609430image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:41.272908image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:44.115276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:46.872080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:49.851181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:53.197414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:55.843898image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T01:59:58.616311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:01.318048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:03.694688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:06.889857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:09.472379image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:12.117862image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:14.827312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:17.226937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:19.674556image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:22.253056image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:24.924527image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:27.496053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:30.352414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:34.244745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:36.772543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:39.161177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:42.340741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:44.847538image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:47.419097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:49.759756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:52.139394image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:00:54.506037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-10-22T02:01:33.084847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-10-22T02:01:33.711618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-10-22T02:01:34.215329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-10-22T02:01:34.979916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-10-22T02:01:00.605477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-10-22T02:01:06.505892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexNature mutationCode type localValeur fonciereLatitudeLongitudeSurface reelle batiNombre pieces principalesSurface terrain
00Vente1293000.048.3271994.0164321135898
11Vente1208050.048.3271994.016432984613
22Vente1176000.048.3271994.016432793610
33Vente5176000.048.3271994.01643200224
44Vente5176000.048.3271994.016432001348
55Vente1213400.048.3271994.01643210041118
66Vente1213400.048.3271994.0164321004500
77Vente1226500.048.3271994.0164321095786
88Vente41.048.3271994.016432112105854
99Vente5267000.048.3271994.01643200160

Last rows

df_indexNature mutationCode type localValeur fonciereLatitudeLongitudeSurface reelle batiNombre pieces principalesSurface terrain
21780782407031Vente427200000.048.8679032.3441071040354
21780792407032Vente427200000.048.8679032.3441072530354
21780802407033Vente227200000.048.8679032.344107202354
21780812407034Vente427200000.048.8679032.3441072380354
21780822407035Vente427200000.048.8679032.344107220354
21780832407036Vente427200000.048.8679032.344107460354
21780842407037Vente427200000.048.8679032.3441071020354
21780852407038Vente427200000.048.8679032.344107440354
21780862407039Vente427200000.048.8679032.3441072280354
21780872407040Vente2680000.048.8679032.3441077230